Addressing Source Separation and Identification issues in surface EMG using Blind Source Separation

Abstract

Source separation and identification is one of the challenging areas in the bio signal processing. The processing of Electromyographic (EMG) signals can be viewed as the identification and separation of a series of overlapping sources of muscle activity with slowly varying source distribution and/or levels of activity. Blind source separation (BSS) techniques such as independent component analysis (ICA) lend themselves well to the analysis of such problems. The problem, however, still remains largely ill-posed even through the use of powerful assumptions such as those posed in ICA and other such techniques. It is generally the case in EMG signals that a certain level of a priori knowledge is..